def test_class_weight(queue):
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
y = np.array([1, 1, 1, 2, 2, 2])
clf = SVC(class_weight={1: 0.1})
clf.fit(X, y, queue=queue)
assert_array_almost_equal(clf.predict(X, queue=queue), [2] * 6)
def _test_libsvm_parameters(queue, array_constr, dtype):
X = array_constr([[-2, -1], [-1, -1], [-1, -2],
[1, 1], [1, 2], [2, 1]], dtype=dtype)
y = array_constr([1, 1, 1, 2, 2, 2], dtype=dtype)
clf = SVC(kernel='linear').fit(X, y, queue=queue)
assert_array_equal(clf.dual_coef_, [[-0.25, .25]])
assert_array_equal(clf.support_, [1, 3])
assert_array_equal(clf.support_vectors_, (X[1], X[3]))
assert_array_equal(clf.intercept_, [0.])
assert_array_equal(clf.predict(X), y)
def test_svc_sigmoid(queue, dtype):
X_train = np.array([[-1, 2], [0, 0], [2, -1],
[+1, +1], [+1, +2], [+2, +1]], dtype=dtype)
X_test = np.array([[0, 2], [0.5, 0.5],
[0.3, 0.1], [2, 0], [-1, -1]], dtype=dtype)
y_train = np.array([1, 1, 1, 2, 2, 2], dtype=dtype)
svc = SVC(kernel='sigmoid').fit(X_train, y_train, queue=queue)
assert_array_equal(svc.dual_coef_, [[-1, -1, -1, 1, 1, 1]])
assert_array_equal(svc.support_, [0, 1, 2, 3, 4, 5])
assert_array_equal(svc.predict(X_test, queue=queue), [2, 2, 1, 2, 1])
